High School Teachers’ Experiences of Consumer Technologies for Stress Management During the COVID-19 Pandemic: Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: Stress in education is an adverse reaction that teachers have to excessive pressures or other types of demands placed on them. Consumer digital technologies are already being used by teachers for stress management, albeit not in a systematic way. Understanding teachers' experiences and the long-term use of technologies to support stress self-management in the educational context is essential for meaningful insight into the value, opportunity, and benefits of use. OBJECTIVE: The aim of this study was first to understand teachers' experiences of consumer technologies for stress management. They were chosen by teachers from a taxonomy tailored to their stress management. The second aim was to explore whether their experiences of use evolved over time as teachers transitioned from working at home during lockdown to working full time on school premises. METHODS: A longitudinal study intended for 6 weeks in the summer term (2020) was extended because of COVID-19 into the autumn term, lasting up to 27 weeks. Teachers chose to use a Withings smartwatch or the Wysa, Daylio, or Teacher Tapp apps. In total, 2 semistructured interviews and web-based surveys were conducted with 8 teachers in South London in the summer term, and 6 (75%) of them took part in a third interview in the autumn term. The interviews were analyzed by creating case studies and conducting cross-case analysis. RESULTS: The teachers described that the data captured or shared by the technology powerfully illustrated the physical and psychosocial toll of their work. This insight gave teachers permission to destress and self-care. The social-emotional confidence generated also led to empathy toward colleagues, and a virtuous cycle of knowledge, self-compassion, permission, and stress management action was demonstrated. Although the COVID-19 pandemic added a new source of stress, it also meant that teachers' stress management experiences could be contrasted between working from home and then back in school. More intentional self-care was demonstrated when back in school, sometimes without the need to refer to the data or technology. CONCLUSIONS: The findings of this study demonstrate that taking a situated approach to understand the real-world, existential significance and value of data generates contextually informed insights. Where a strategic personal choice of consumer technology is enabled for high school heads of year, the data generated are perceived as holistic, with personal and professional salience, and are motivational in the educational context. Technology adoption was aided by the pandemic conditions of home working, and this flexibility would otherwise need workplace facilitation. These findings add to the value proposition of technologies for individual stress management and workforce health outcomes pertinent to educators, policy makers, and designers.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,008 | 0,006 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».